Conventional signal sampling is typically performed periodically at a fixed frequency. According to the Nyquist sampling theorem, the minimum sampling rate is twice the bandwidth of a signal. Although such a sampling approach can ensure perfect restoration of the signal, hardware resources and storage space are wasted to some extent. In recent years, with the introduction of compressive sensing, the limitation imposed by the Nyquist sampling theorem has been broken. Exploration of sampling approaches which enable sampling at a rate lower than the Nyquist frequency has begun.
An existing sampler that samples at a rate lower than the Nyquist frequency is called analog to information converter (AIC), which employs a random sampler that performs frequency mixing using a pseudo random sequence. As illustrate in FIG. 1, the random sampler first performs frequency mixing of a sparse input signal with the pseudo random sequence, then samples the signal at a low frequency using a conventional integral sampler, which performs periodic sampling, and next transmits the sampled signal to a subsequent compressive sensing algorithm.
However, the analog to information converter merely mixes the input signal with a sequence consisting of +1 and −1, instead of realizing true random sampling. Meanwhile, its hardware complexity is relatively high, and a certain amount of storage space is needed. Further, for a one-dimension slow-varying signal, frequency mixing will increase the complexity of the signal. Therefore, the analog to information converter is not adapted to random sampling of the one-dimension slow-varying signal.
Accordingly, a random sampler which is more adapted to a one-dimension slow-varying signal is desired.